Update Inductor windows tutorial with xpu support

prototype_source/inductor_windows.rst

@@ -0,0 +1,103 @@
+How to use ``torch.compile`` on Windows CPU/XPU
+===============================================
+
+**Author**: `Zhaoqiong Zheng <https://github.com/ZhaoqiongZ>`_, `Xu, Han <https://github.com/xuhancn>`_
+
+
+Introduction
+------------
+
+TorchInductor is the new compiler backend that compiles the FX Graphs generated by TorchDynamo into optimized C++/Triton kernels.
+
+This tutorial introduces the steps for using TorchInductor via ``torch.compile`` on Windows CPU/XPU.
+
+
+Software Installation
+---------------------
+
+Now, we will walk you through a step-by-step tutorial for how to use ``torch.compile`` on Windows CPU/XPU.
+
+Install a Compiler
+^^^^^^^^^^^^^^^^^^
+
+C++ compiler is required for TorchInductor optimization, let's take Microsoft Visual C++ (MSVC) as an example.
+
+1. Download and install `MSVC <https://visualstudio.microsoft.com/downloads/>`_.
+
+1. During Installation, select **Workloads** and then **Desktop & Mobile**.
+1. Select a checkmark on **Desktop Development with C++** and install.
+
+.. image:: ../_static/img/install_msvc.png
+
+
+.. note::
+
+    Windows CPU inductor also support C++ compiler `LLVM Compiler <https://github.com/llvm/llvm-project/releases>`_ and `Intel Compiler <https://www.intel.com/content/www/us/en/developer/tools/oneapi/dpc-compiler-download.html>`_ for better performance.
+    Please check `Alternative Compiler for better performance on CPU <#alternative-compiler-for-better-performance>`_.
+
+Set Up Environment
+^^^^^^^^^^^^^^^^^^
+Next, let's configure our environment.
+
+#. Open a command line environment via cmd.exe.
+#. Activate ``MSVC`` via below command::
+
+    "C:/Program Files/Microsoft Visual Studio/2022/Community/VC/Auxiliary/Build/vcvars64.bat"
+#. Create and activate a virtual environment: ::
+#. Install `PyTorch 2.5 <https://pytorch.org/get-started/locally/>`_ or later for CPU Usage. Install PyTorch 2.7 or later refer to `Getting Started on Intel GPU <https://pytorch.org/docs/main/notes/get_start_xpu.html>`_ for XPU usage.
+#. Here is an example of how to use TorchInductor on Windows:
+.. code-block:: python
+
+    import torch
+    device="cpu" # or "xpu" for XPU
+    def foo(x, y):
+        a = torch.sin(x)
+        b = torch.cos(x)
+        return a + b
+    opt_foo1 = torch.compile(foo)
+    print(opt_foo1(torch.randn(10, 10).to(device), torch.randn(10, 10).to(device)))
+
+#. Below is the output of the above example::
+
+    tensor([[-3.9074e-02,  1.3994e+00,  1.3894e+00,  3.2630e-01,  8.3060e-01,
+            1.1833e+00,  1.4016e+00,  7.1905e-01,  9.0637e-01, -1.3648e+00],
+            [ 1.3728e+00,  7.2863e-01,  8.6888e-01, -6.5442e-01,  5.6790e-01,
+            5.2025e-01, -1.2647e+00,  1.2684e+00, -1.2483e+00, -7.2845e-01],
+            [-6.7747e-01,  1.2028e+00,  1.1431e+00,  2.7196e-02,  5.5304e-01,
+            6.1945e-01,  4.6654e-01, -3.7376e-01,  9.3644e-01,  1.3600e+00],
+            [-1.0157e-01,  7.7200e-02,  1.0146e+00,  8.8175e-02, -1.4057e+00,
+            8.8119e-01,  6.2853e-01,  3.2773e-01,  8.5082e-01,  8.4615e-01],
+            [ 1.4140e+00,  1.2130e+00, -2.0762e-01,  3.3914e-01,  4.1122e-01,
+            8.6895e-01,  5.8852e-01,  9.3310e-01,  1.4101e+00,  9.8318e-01],
+            [ 1.2355e+00,  7.9290e-02,  1.3707e+00,  1.3754e+00,  1.3768e+00,
+            9.8970e-01,  1.1171e+00, -5.9944e-01,  1.2553e+00,  1.3394e+00],
+            [-1.3428e+00,  1.8400e-01,  1.1756e+00, -3.0654e-01,  9.7973e-01,
+            1.4019e+00,  1.1886e+00, -1.9194e-01,  1.3632e+00,  1.1811e+00],
+            [-7.1615e-01,  4.6622e-01,  1.2089e+00,  9.2011e-01,  1.0659e+00,
+            9.0892e-01,  1.1932e+00,  1.3888e+00,  1.3898e+00,  1.3218e+00],
+            [ 1.4139e+00, -1.4000e-01,  9.1192e-01,  3.0175e-01, -9.6432e-01,
+            -1.0498e+00,  1.4115e+00, -9.3212e-01, -9.0964e-01,  1.0127e+00],
+            [ 5.7244e-04,  1.2799e+00,  1.3595e+00,  1.0907e+00,  3.7191e-01,
+            1.4062e+00,  1.3672e+00,  6.8502e-02,  8.5216e-01,  8.6046e-01]])
+
+Alternative Compiler for better performance on CPU
+--------------------------------------------------
+
+To enhance performance for inductor on Windows CPU, you can use the Intel Compiler or LLVM Compiler. However, they rely on the runtime libraries from Microsoft Visual C++ (MSVC). Therefore, your first step should be to install MSVC.
+
+Intel Compiler
+^^^^^^^^^^^^^^
+
+#. Download and install `Intel Compiler <https://www.intel.com/content/www/us/en/developer/tools/oneapi/dpc-compiler-download.html>`_ with Windows version.
+#. Set Windows Inductor Compiler via environment variable ``set CXX=icx-cl``.
+
+LLVM Compiler
+^^^^^^^^^^^^^
+
+#. Download and install `LLVM Compiler <https://github.com/llvm/llvm-project/releases>`_ and choose win64 version.
+#. Set Windows Inductor Compiler via environment variable ``set CXX=clang-cl``.
+
+Conclusion
+----------
+
+In this tutorial, we introduce how to use Inductor on Windows CPU with PyTorch 2.5 or later, and on Windows XPU with PyTorch 2.7 or later. We can also use Intel Compiler or LLVM Compiler to get better performance on CPU.

prototype_source/inductor_windows_cpu.rst

@@ -1,130 +1,7 @@
-How to use TorchInductor on Windows CPU
-=======================================
+This tutorial has been moved to https://pytorch.org/tutorials/prototype/inductor_windows.html.
 
-**Author**: `Zhaoqiong Zheng <https://github.com/ZhaoqiongZ>`_, `Xu, Han <https://github.com/xuhancn>`_
+Redirecting in 3 seconds...
 
+.. raw:: html
 
-
-TorchInductor is a compiler backend that transforms FX Graphs generated by TorchDynamo into highly optimized C++/Triton kernels.
-This tutorial will guide you through the process of using TorchInductor on a Windows CPU.
-
-.. grid:: 2
-
-    .. grid-item-card:: :octicon:`mortar-board;1em;` What you will learn
-       :class-card: card-prerequisites
-
-       * How to compile and execute a Python function with PyTorch, optimized for Windows CPU
-       * Basics of TorchInductor's optimization using C++/Triton kernels.
-
-    .. grid-item-card:: :octicon:`list-unordered;1em;` Prerequisites
-       :class-card: card-prerequisites
-
-       * PyTorch v2.5 or later
-       * Microsoft Visual C++ (MSVC)
-       * Miniforge for Windows
-
-Install the Required Software
------------------------------
-
-First, let's install the required software. C++ compiler is required for TorchInductor optimization.
-We will use Microsoft Visual C++ (MSVC) for this example. 
-
-1. Download and install `MSVC <https://visualstudio.microsoft.com/downloads/>`_.
-
-2. During the installation, choose **Desktop Development with C++** in the **Desktop & Mobile** section in **Workloads** table. Then install the software
-
-.. note::
-
-     We recommend C++ compiler `Clang <https://github.com/llvm/llvm-project/releases>`_ and `Intel Compiler <https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit-download.html>`_.
-     Please check `Alternative Compiler for better performance <#alternative-compiler-for-better-performance>`_.
-
-3. Download and install `Miniforge3-Windows-x86_64.exe <https://github.com/conda-forge/miniforge/releases/latest/>`__.
-
-Set Up the Environment
-----------------------
-
-#. Open the command line environment via ``cmd.exe``.
-#. Activate ``MSVC`` with the following command:
-
-   .. code-block:: sh
-
-    "C:/Program Files/Microsoft Visual Studio/2022/Community/VC/Auxiliary/Build/vcvars64.bat"
-#. Activate ``conda`` with the following command:
-
-   .. code-block:: sh
-
-    "C:/ProgramData/miniforge3/Scripts/activate.bat"
-#. Create and activate a custom conda environment:
-
-   .. code-block:: sh
-
-    conda create -n inductor_cpu_windows python=3.10 -y 
-    conda activate inductor_cpu_windows
-
-#. Install `PyTorch 2.5 <https://pytorch.org/get-started/locally/>`_ or later.
-
-Using TorchInductor on Windows CPU
-----------------------------------
-
-Here’s a simple example to demonstrate how to use TorchInductor:
-
-.. code-block:: python
-
-
-    import torch
-    def foo(x, y):
-        a = torch.sin(x)
-        b = torch.cos(y)
-        return a + b
-    opt_foo1 = torch.compile(foo)
-    print(opt_foo1(torch.randn(10, 10), torch.randn(10, 10)))
-
-Here is the sample output that this code might return: 
-
-.. code-block:: sh
-
-    tensor([[-3.9074e-02,  1.3994e+00,  1.3894e+00,  3.2630e-01,  8.3060e-01,
-            1.1833e+00,  1.4016e+00,  7.1905e-01,  9.0637e-01, -1.3648e+00],
-            [ 1.3728e+00,  7.2863e-01,  8.6888e-01, -6.5442e-01,  5.6790e-01,
-            5.2025e-01, -1.2647e+00,  1.2684e+00, -1.2483e+00, -7.2845e-01],
-            [-6.7747e-01,  1.2028e+00,  1.1431e+00,  2.7196e-02,  5.5304e-01,
-            6.1945e-01,  4.6654e-01, -3.7376e-01,  9.3644e-01,  1.3600e+00],
-            [-1.0157e-01,  7.7200e-02,  1.0146e+00,  8.8175e-02, -1.4057e+00,
-            8.8119e-01,  6.2853e-01,  3.2773e-01,  8.5082e-01,  8.4615e-01],
-            [ 1.4140e+00,  1.2130e+00, -2.0762e-01,  3.3914e-01,  4.1122e-01,
-            8.6895e-01,  5.8852e-01,  9.3310e-01,  1.4101e+00,  9.8318e-01],
-            [ 1.2355e+00,  7.9290e-02,  1.3707e+00,  1.3754e+00,  1.3768e+00,
-            9.8970e-01,  1.1171e+00, -5.9944e-01,  1.2553e+00,  1.3394e+00],
-            [-1.3428e+00,  1.8400e-01,  1.1756e+00, -3.0654e-01,  9.7973e-01,
-            1.4019e+00,  1.1886e+00, -1.9194e-01,  1.3632e+00,  1.1811e+00],
-            [-7.1615e-01,  4.6622e-01,  1.2089e+00,  9.2011e-01,  1.0659e+00,
-            9.0892e-01,  1.1932e+00,  1.3888e+00,  1.3898e+00,  1.3218e+00],
-            [ 1.4139e+00, -1.4000e-01,  9.1192e-01,  3.0175e-01, -9.6432e-01,
-            -1.0498e+00,  1.4115e+00, -9.3212e-01, -9.0964e-01,  1.0127e+00],
-            [ 5.7244e-04,  1.2799e+00,  1.3595e+00,  1.0907e+00,  3.7191e-01,
-            1.4062e+00,  1.3672e+00,  6.8502e-02,  8.5216e-01,  8.6046e-01]])
-
-Using an Alternative Compiler for Better Performance
--------------------------------------------
-
-To enhance performance on Windows inductor, you can use the Intel Compiler or LLVM Compiler. However, they rely on the runtime libraries from Microsoft Visual C++ (MSVC). Therefore, your first step should be to install MSVC.
-
-Intel Compiler
-^^^^^^^^^^^^^^
-
-#. Download and install `Intel Compiler <https://www.intel.com/content/www/us/en/developer/tools/oneapi/dpc-compiler-download.html>`_ with Windows version.
-#. Set Windows Inductor Compiler with the CXX environment variable ``set CXX=icx-cl``.
-
-Intel also provides a comprehensive step-by-step guide, complete with performance data. Please check `Intel® oneAPI DPC++/C++ Compiler Boosts PyTorch* Inductor Performance on Windows* for CPU Devices <https://www.intel.com/content/www/us/en/developer/articles/technical/boost-pytorch-inductor-performance-on-windows.html>`_.
-
-LLVM Compiler
-^^^^^^^^^^^^^
-
-#. Download and install `LLVM Compiler <https://github.com/llvm/llvm-project/releases>`_ and choose win64 version.
-#. Set Windows Inductor Compiler with the CXX environment variable ``set CXX=clang-cl``.
-
-Conclusion
-----------
-
-In this tutorial, we have learned how to use Inductor on Windows CPU with PyTorch. In addition, we discussed
-further performance improvements with Intel Compiler and LLVM Compiler.
+   <meta http-equiv="Refresh" content="3; url='https://pytorch.org/tutorials/prototype/inductor_windows.html'" />

prototype_source/prototype_index.rst

@@ -228,7 +228,7 @@ Prototype features are not available as part of binary distributions like PyPI o
    :header: Inductor Windows CPU Tutorial
    :card_description: Speed up your models with Inductor On Windows CPU
    :image: ../_static/img/thumbnails/cropped/generic-pytorch-logo.png
-   :link: ../prototype/inductor_windows_cpu.html
+   :link: ../prototype/inductor_windows.html
    :tags: Model-Optimization
 
 .. customcarditem::
@@ -286,7 +286,7 @@ Prototype features are not available as part of binary distributions like PyPI o
    prototype/flight_recorder_tutorial.html
    prototype/graph_mode_dynamic_bert_tutorial.html
    prototype/inductor_cpp_wrapper_tutorial.html
-   prototype/inductor_windows_cpu.html
+   prototype/inductor_windows.html
    prototype/pt2e_quantizer.html
    prototype/pt2e_quant_ptq.html
    prototype/pt2e_quant_qat.html